Identification of sex hormone-binding globulin in the human hypothalamus

Gonadal steroids are known to influence hypothalamic functions through both genomic and non-genomic pathways. Sex hormone-binding globulin ( SHBG) may act by a non-genomic mechanism independent of classical steroid receptors. Here we describe the immunocytochemical mapping of SHBG-containing neurons and nerve fibers in the human hypothalamus and infundibulum. Mass spectrometry and Western blot analysis were also used to characterize the biochemical characteristics of SHBG in the hypothalamus and cerebrospinal fluid (CSF) of humans. SHBG-immunoreactive neurons were observed in the supraoptic nucleus, the suprachiasmatic nucleus, the bed nucleus of the stria terminalis, paraventricular nucleus, arcuate nucleus, the perifornical region and the medial preoptic area in human brains. There were SHBG-immunoreactive axons in the median eminence and the infundibulum. A partial colocalization with oxytocin could be observed in the posterior pituitary lobe in consecutive semithin sections. We also found strong immunoreactivity for SHBG in epithelial cells of the choroid plexus and in a portion of the ependymal cells lining the third ventricle. Mass spectrometry showed that affinity-purified SHBG from the hypothalamus and choroid plexus is structurally similar to the SHBG identified in the CSF. The multiple localizations of SHBG suggest neurohypophyseal and neuroendocrine functions. The biochemical data suggest that CSF SHBG is of brain rather than blood origin. Copyright (c) 2005 S. Karger AG, Base

The effects of interactive training of healthcare providers on the management of life-threatening emergencies in hospital

Background Preparing healthcare providers to manage relatively rare life‐threatening emergency situations effectively is a challenge. Training sessions enable staff to rehearse for these events and are recommended by several reports and guidelines. In this review we have focused on interactive training, this includes any element where the training is not solely didactic but provides opportunity for discussions, rehearsals, or interaction with faculty or technology. It is important to understand the effective methods and essential elements for successful emergency training so that resources can be appropriately targeted to improve outcomes. Objectives To assess the effects of interactive training of healthcare providers on the management of life‐threatening emergencies in hospital on patient outcomes, clinical care practices, or organisational practices, and to identify essential components of effective interactive emergency training programmes. Search methods We searched CENTRAL, MEDLINE, Embase, CINAHL and ERIC and two trials registers up to 11 March 2019. We searched references of included studies, conference proceedings, and contacted study authors. Selection criteria We included randomised trials and cluster‐randomised trials comparing interactive training for emergency situations with standard/no training. We defined emergency situations as those in which immediate lifesaving action is required, for example cardiac arrests and major haemorrhage. We included all studies where healthcare workers involved in providing direct clinical care were participants. We excluded studies outside of a hospital setting or where the intervention was not targeted at practicing healthcare workers. We included trials irrespective of publication status, date, and language. Data collection and analysis We used standard methodological procedures expected by Cochrane and Cochrane Effective Practice and Organisation of Care (EPOC) Group. Two review authors independently extracted data and assessed the risk of bias of each included trial. Due to the small number of studies and the heterogeneity in outcome measures, we were unable to perform the planned meta‐analysis. We provide a structured synthesis for the following outcomes: survival to hospital discharge, morbidity rate, protocol or guideline adherence, patient outcomes, clinical practice outcomes, and organisation‐of‐care outcomes. We used the GRADE approach to rate the certainty of the evidence and the strength of recommendations for each outcome. Main results We included 11 studies that reported on 2000 healthcare providers and over 300,000 patients; one study did not report the number of participants. Seven were cluster randomised trials and four were single centre studies. Four studies focused on obstetric training, three on obstetric and neonatal care, two on neonatal training, one on trauma and one on general resuscitations. The studies were spread across high‐, middle‐ and low‐income settings. Interactive training may make little or no difference in survival to hospital discharge for patients requiring resuscitation (1 study; 30 participants; 98 events; low‐certainty evidence). We are uncertain if emergency training changes morbidity rate, as the certainty of the evidence is very low (3 studies; 1778 participants; 57,193 patients, when reported). We are uncertain if training alters healthcare providers' adherence to clinical protocols or guidelines, as the certainty of the evidence is very low (3 studies; 156 participants; 558 patients). We are uncertain if there were improvements in patient outcomes following interactive training for emergency situations, as we assessed the evidence as very low‐certainty (5 studies, 951 participants; 314,055 patients). We are uncertain if training for emergency situations improves clinical practice outcomes as the certainty of the evidence is very low (4 studies; 1417 participants; 28,676 patients, when reported). Two studies reported organisation‐of‐care outcomes, we are uncertain if interactive emergency training has any effect on this outcome as the certainty of the evidence is very low (634 participants; 179,400 patient population). We examined prespecified subgroups and found no clear commonalities in effect of multidisciplinary training, location of training, duration of the course, or duration of follow‐up. We also examined areas arising from the studies including focus of training, proportion of staff trained, leadership of intervention, and incentive/trigger to participate, and again identified no clear mediating factors. The sources of funding for the studies were governmental, local organisations, or philanthropic donors. Authors' conclusions We are uncertain if there are any benefits of interactive training of healthcare providers on the management of life‐threatening emergencies in hospital as the certainty of the evidence is very low. We were unable to identify any factors that may have allowed us to identify an essential element of these interactive training courses. We found a lack of consistent reporting, which contributed to the inability to meta‐analyse across specialities. More trials are required to build the evidence base for the optimum way to prepare healthcare providers for rare life‐threatening emergency events. These trials need to be conducted with attention to outcomes important to patients, healthcare providers, and policymakers. It is vitally important to develop high‐quality studies adequately powered and with attention to minimising the risk of bias

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group

Reflexive learning, socio-cognitive conflict and peer-assessment to improve the quality of feedbacks in online tests

International audienceOur previous works have introduced the Tsaap-notes platform dedicated to the semi automatic generation of multiple choice questionnaire providing feedbacks: it reuses interactive questions asked by teachers during lectures, as well as the notes taken by students after the presentation of the results as feedbacks integrated into the quizzes. In this paper, we introduce a new feature which aims at increasing the number of contributions of students in order to significantly improve the quality of the feedbacks used in the resulting quizzes. This feature splits the submission of an answer into several distinct phases to harvest explanations given by students, and then applies an algorithm to filter the best contributions to be integrated as feedbacks in the tests. Our approach has been validated by a first experimentation involving master students enrolled in a computer science course

Network meta-analysis-highly attractive but more methodological research is needed

Network meta-analysis, in the context of a systematic review, is a meta-analysis in which multiple treatments (that is, three or more) are being compared using both direct comparisons of interventions within randomized controlled trials and indirect comparisons across trials based on a common comparator. To ensure validity of findings from network meta-analyses, the systematic review must be designed rigorously and conducted carefully. Aspects of designing and conducting a systematic review for network meta-analysis include defining the review question, specifying eligibility criteria, searching for and selecting studies, assessing risk of bias and quality of evidence, conducting a network meta-analysis, interpreting and reporting findings. This commentary summarizes the methodologic challenges and research opportunities for network meta-analysis relevant to each aspect of the systematic review process based on discussions at a network meta-analysis methodology meeting we hosted in May 2010 at the Johns Hopkins Bloomberg School of Public Health. Since this commentary reflects the discussion at that meeting, it is not intended to provide an overview of the field

Interventions to prevent spontaneous preterm birth in high-risk women with singleton pregnancy: A systematic review and network meta-analysis

© 2019 The Cochrane Collaboration. This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To compare the efficacy of current, relevant interventions to prevent preterm birth in women with singleton pregnancy and high individual risk of spontaneous preterm birth. We will consider interventions for women with a history of spontaneous preterm birth or short cervical length and women with asymptomatic vaginal infections

Pregabalin versus gabapentin in partial epilepsy: a meta-analysis of dose-response relationships

<p>Abstract</p> <p>Background</p> <p>To compare the efficacy of pregabalin and gabapentin at comparable effective dose levels in patients with refractory partial epilepsy.</p> <p>Methods</p> <p>Eight randomized placebo controlled trials investigating the efficacy of pregabalin (4 studies) and gabapentin (4 studies) over 12 weeks were identified with a systematic literature search. The endpoints of interest were "responder rate" (where response was defined as at least a 50% reduction from baseline in the number of seizures) and "change from baseline in seizure-free days over the last 28 days (SFD)". Results of all trials were analyzed using an indirect comparison approach with placebo as the common comparator. The base-case analysis used the intention-to-treat last observation carried forward method. Two sensitivity analyses were conducted among completer and responder populations.</p> <p>Results</p> <p>The base-case analysis revealed statistically significant differences in response rate in favor of pregabalin 300 mg versus gabapentin 1200 mg (odds ratio, 1.82; 95% confidence interval, 1.02, 3.25) and pregabalin 600 mg versus gabapentin 1800 mg (odds ratio, 2.52; 95% confidence interval, 1.21, 5.27). Both sensitivity analyses supported the findings of the base-case analysis, although statistical significance was not demonstrated. All dose levels of pregabalin (150 mg to 600 mg) were more efficacious than corresponding dosages of gabapentin (900 mg to 2400 mg) in terms of SFD over the last 28 days.</p> <p>Conclusion</p> <p>In patients with refractory partial epilepsy, pregabalin is likely to be more effective than gabapentin at comparable effective doses, based on clinical response and the number of SFD.</p

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption

Background In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency. Methods The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets. Results Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated. Conclusions Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C1178). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript